DUEL: Dempster Uncertainty-Based Enhanced- Trust Level Scheme for VANET

Abstract

This work presents an uncertainty-based Trust Model (TM). It uses Dempster Shafer Theory (DST) to cater to uncertainty arising due to information scarcity in Vehicular Adhoc Networks (VANETs). DST combines direct and indirect trust values of vehicles sending messages to build its new trust opinion. Malicious vehicles, albeit authenticated, may alter or drop messages. The solution is to establish trust. Trust establishment assumes that vehicle behaviour is predictable, and its past transactions are useful for anticipating its future response. The high vehicular mobility, dynamic topology, and low neighbourhood density in VANETs cause a lack of credible information for establishing trust. The work also presents the introduction of the penalty function, forgetting function, rewarding factor, and the uncertainty-based importance factor during the combination of direct and indirect trust values using DST. Apart from this, the forgiving factor deals with a situation when a distrusted vehicle starts behaving ideally. Additionally, we present trust contexts based on types of messages altered or dropped by them. Four different trust levels and uncertainty in DUEL aids the receiver make correct decisions during an attack. DUEL is applied and tested on the real-time data of vehicle trajectory. Results show that DUEL can capture both false message attacks and message suppression attacks even in rapidly changing neighbourhoods and performs well in terms of high values of TPR and low values of FPR.